Tunnel harmful gas detection method and system and storage medium

Abstract

The invention discloses a tunnel harmful gas detection method and system and a storage medium, and relates to the technical field of tunnel engineering. The tunnel harmful gas detection method comprises the following steps: preliminarily detecting a geological high-risk area in a tunnel; determining a harmful gas leakage source in the geological high-risk area; monitoring the concentration of the harmful gas at the harmful gas leakage source; determining whether alarm information is generated or not according to the concentration of the harmful gas, and giving an alarm according to the alarm information; and the operation is periodically repeated. According to the method, the geological high-risk area in the tunnel is preliminarily detected, the harmful gas leakage source is determined in the geological high-risk area, the concentration of the harmful gas at the harmful gas leakage source is continuously monitored, the leakage condition of the harmful gas can be captured in real time, and the problem that sudden gas leakage cannot be found in time is avoided.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel engineering, and specifically relates to a method, a system and a storage medium for detecting harmful gases in a tunnel. Background Art

[0002] In tunnel engineering, the presence of harmful gases in the tunnel poses a serious threat to the health of personnel and the safety of the project. For example, methane is one of the common harmful gases in tunnel construction. It has the characteristics of being flammable and explosive. Once the concentration is too high, it is extremely easy to cause explosion accidents. In addition, harmful gases such as hydrogen sulfide and carbon monoxide are also common in tunnels. These gases not only cause harm to human health, such as poisoning and asphyxiation, but may also cause fires or explosions, bringing huge potential safety hazards to tunnel engineering. Therefore, in tunnel engineering, it is necessary to detect harmful gases in the tunnel. When the concentration of harmful gases reaches a certain amount, it is necessary to turn on the exhaust fan for ventilation, so as to reduce the concentration of harmful gases in the tunnel and ensure the safety of tunnel engineering.

[0003] Currently, the methods for detecting harmful gases in tunnels mainly include: electrochemical sensor detection and laser spectroscopy detection, etc. Electrochemical sensor detection is to set up multiple sensors in the tunnel, and determine the concentration of harmful gases by measuring the current change generated by the oxidation-reduction reaction of harmful gases on the electrode surface of the sensor. Laser spectroscopy detection is to preset monitoring points in the tunnel and emit laser light to the monitoring points, and identify the components and concentration of harmful gases by using the spectral characteristics generated by the interaction between the laser and harmful gas molecules.

[0004] However, the existing detection methods have the following disadvantages: 1. The existing detection methods rely on manual inspections or fixed sensor networks, and cannot track sudden gas leaks in real time, making it difficult to dynamically adjust the key monitoring areas; 2. Faults, fissures and other geological anomalies are the main channels for the migration of harmful gases, while the existing detection methods cannot efficiently identify geological anomalies and the distribution of harmful gases, resulting in insufficient detection pertinence; 3. Geological exploration and harmful gas detection are usually implemented in stages, and it is necessary to repeatedly deploy equipment, resulting in an extended operation cycle and resource waste. Summary of the Invention

[0005] The purpose of the present application is to provide a method, a system and a storage medium for detecting harmful gases in a tunnel, so as to solve the problem that the existing detection methods cannot track sudden gas leaks in real time.

[0006] The technical solution adopted by the present application to solve its technical problems is:

[0007] In the first aspect, a method for detecting harmful gases in a tunnel is provided, including:

[0008] Preliminarily detect the geological high-risk areas in the tunnel;

[0009] Determine the harmful gas leakage source within the geological high-risk area;

[0010] Monitor the harmful gas concentration at the harmful gas leakage source;

[0011] Determine whether to generate an alarm message based on the harmful gas concentration, and issue an alarm according to the alarm message;

[0012] Repeat the above operations periodically.

[0013] Furthermore, the preliminary detection of the geological high-risk area in the tunnel includes:

[0014] Arrange a seismic source device and a seismic wave receiver in the tunnel;

[0015] Trigger the seismic source device to emit seismic waves, and use the seismic wave receiver to collect the reflected wave signals;

[0016] Process the reflected wave signals and generate a geological anomaly distribution map;

[0017] Generate a geological high-risk area based on the geological anomaly distribution map.

[0018] Furthermore, the processing of the reflected wave signals and generating a geological anomaly distribution map includes:

[0019] Perform wavelet transform on the reflected wave signals to separate high-frequency noise;

[0020] Perform envelope analysis on the waveform to detect waveform distortion characteristics;

[0021] Determine geological anomalies based on the waveform distortion characteristics and generate a geological anomaly distribution map.

[0022] Furthermore, the determination of the harmful gas leakage source within the geological high-risk area includes:

[0023] Arrange a laser scanning device in the tunnel and set the scanning path;

[0024] Use the laser scanning device to scan the geological high-risk area, receive the reflected light signals, and record the absorption spectrum data;

[0025] Analyze the absorption spectrum data and calculate the harmful gas concentration to obtain several discrete point concentration data of harmful gases;

[0026] Generate a three-dimensional gas concentration model within the geological high-risk area based on the several discrete point concentration data of harmful gases;

[0027] Locate the harmful gas leakage source based on the three-dimensional gas concentration model.

[0028] Furthermore, the monitoring of the harmful gas concentration at the harmful gas leakage source includes:

[0029] Laser gas sensors are arranged around the harmful gas leakage source, and the laser gas sensors are used to collect the harmful gas concentration data at the harmful gas leakage source in real time;

[0030] According to the fluctuation degree of the harmful gas concentration data collected by the laser gas sensor, the monitoring mode of the laser gas sensor is adjusted.

[0031] Furthermore, the monitoring of the harmful gas concentration at the harmful gas leakage source further includes:

[0032] The detection data of the geological high-risk area, the determination data of the harmful gas leakage source and the real-time monitoring data of the laser gas sensor are input into the AI model, and the subsequent arrangement mode of the laser gas sensor is optimized by using the AI model.

[0033] Furthermore, the determination of whether to generate an alarm message according to the harmful gas concentration includes:

[0034] When the harmful gas concentration is equal to or greater than the first threshold, a first alarm message is generated;

[0035] When the harmful gas concentration is equal to or greater than the second threshold, a second alarm message is generated;

[0036] Among them, the first threshold is less than the second threshold.

[0037] In a second aspect, a tunnel harmful gas detection system is provided, including:

[0038] A detection module for preliminarily detecting the geological high-risk area in the tunnel;

[0039] A determination module for determining the harmful gas leakage source in the geological high-risk area;

[0040] A monitoring module for monitoring the harmful gas concentration at the harmful gas leakage source;

[0041] An alarm module for determining whether to generate an alarm message according to the harmful gas concentration and giving an alarm according to the alarm message.

[0042] In a third aspect, a tunnel harmful gas detection system is provided, including a memory and a processor;

[0043] The memory stores instructions executable by the processor;

[0044] When the processor is configured to execute the instructions, the system implements the tunnel harmful gas detection method provided in the first aspect.

[0045] Fourthly, a computer storage medium is provided, including computer instructions, which, when running on a computer, enable the computer to execute the tunnel harmful gas detection method provided in the first aspect.

[0046] Advantages of the present application:

[0047] The tunnel harmful gas detection method, system and storage medium provided by the embodiments of the present application can initially detect geological high-risk areas in the tunnel, determine harmful gas leakage sources within the geological high-risk areas, and continuously monitor the harmful gas concentration at the harmful gas leakage sources, so as to be able to capture the leakage situation of harmful gases in real time and avoid the problem that sudden gas leakage cannot be detected in time; once the harmful gas concentration reaches the set threshold, an alarm message can be quickly generated and an alarm can be given, providing timely safety warnings for the personnel and equipment in the tunnel and effectively reducing the safety risks brought by harmful gas leakage.

[0048] Compared with the prior art, by determining harmful gas leakage sources within the initially detected geological high-risk areas, the present application can establish a direct connection between geological anomalies and harmful gas distributions, thereby more accurately identifying the sources and migration paths of harmful gases. This can not only concentrate monitoring resources on the areas most likely to have harmful gas leakage, improve the efficiency and pertinence of monitoring, and avoid missed detections or misjudgments caused by insufficient monitoring pertinence, but also effectively reduce the number of monitoring devices deployed and the detection range, shorten the operation cycle, improve engineering efficiency, and avoid waste of resources. Description of the drawings

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 is a flowchart of the tunnel harmful gas detection method provided by the embodiments of the present application;

[0051] Figure 2 is a schematic composition diagram of the tunnel harmful gas detection system provided by the embodiments of the present application;

[0052] Figure 3 is a schematic hardware structure diagram of the tunnel harmful gas detection system provided by the embodiments of the present application.

[0053] Reference numerals:

[0054] 100 - System;

[0055] 101 - Detection module; 102 - Determination module; 103 - Monitoring module; 104 - Alarm module;

[0056] 200 - System;

[0057] 201 - Memory; 202 - Processor; 203 - Communication interface; 204 - Bus. Detailed implementation manner

[0058] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0059] In the description of the present application, the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present application. Without special instructions, in the case of satisfying the relative positional relationship shown in the accompanying drawings, the above-described orientation description can be flexibly set during the actual application process.

[0060] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "set", "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0061] See Figure 1 , the embodiments of the present application provide a method for detecting harmful gases in a tunnel, including the following steps:

[0062] S1. Initially detect the geological high-risk areas in the tunnel.

[0063] Exemplarily, the seismic wave reflection method is used to initially detect the geological high-risk areas. Among them, the geological high-risk areas mainly include areas such as faults and fracture zones, which are the main channels for the migration of harmful gases.

[0064] The method for initially detecting the geological high-risk areas in the tunnel includes the following steps:

[0065] S11. Arrange a seismic source device and seismic wave receivers in the tunnel.

[0066] Exemplarily, install a low-frequency seismic source device with a frequency range of 10 - 200 Hz on the tunnel face or sidewall; arrange seismic wave receivers every 5 - 10 m along the tunnel axis to form a receiving array.

[0067] S12. Trigger the seismic source device to emit seismic waves, and use the seismic wave receivers to collect reflected wave signals.

[0068] Exemplarily, trigger the seismic source device to emit low-frequency seismic waves, such as seismic waves with a main frequency of 50 Hz, use the array-arranged seismic wave receivers to collect reflected wave signals, and transmit the reflected wave signals to the signal processor by wired or wireless means.

[0069] S13. Process the reflected wave signals and generate a geological anomaly distribution map.

[0070] Exemplarily, when the signal processor receives the reflected wave signals, first perform wavelet transform on the original signals to separate high-frequency noise components greater than 500 Hz, and then perform envelope analysis (Hilbert transform) on the waveforms to detect waveform distortion characteristics.

[0071] Among them, the wavelet transform formula is:

[0072] Among them, x(t) is the original seismic signal, ψ(t) is the wavelet basis function, a is the scale parameter (corresponding to frequency), and b is the translation parameter (corresponding to time position).

[0073] Hilbert transform envelope analysis:

[0074] Analytical signal construction: z(t) = x(t) + j·H[x(t)], where H[x(t)] is the Hilbert transform of the original seismic signal x(t).

[0075] The envelope is the modulus value of the analytical signal:

[0076] Determine geological anomalies according to the waveform distortion characteristics; among them, the waveform distortion characteristics include high-frequency noise sudden increase areas and waveform envelope distortion areas. The high-frequency noise sudden increase areas are marked as fracture development areas, and the waveform envelope distortion areas are marked as fault interfaces. Combining these two types of abnormal areas, generate a geological anomaly distribution map.

[0077] S14. Generate a geological high-risk area according to the geological anomaly distribution map.

[0078] S2. Determine the harmful gas leakage source within the geological high-risk area.

[0079] Exemplarily, by using the method of laser spectroscopy for targeted scanning and gas distribution modeling, the harmful gas leakage source is determined within the geological high-risk area. Thus, a direct relationship between geological anomalies and harmful gas distribution can be established, so as to more accurately identify the source and migration path of harmful gases, facilitating subsequent concentration of monitoring resources on the areas most likely to have gas leakage, and improving the monitoring efficiency and pertinence.

[0080] The method for determining the harmful gas leakage source in the geological high-risk area includes the following steps:

[0081] S21. Arrange a laser scanning device in the tunnel and set a scanning path.

[0082] Exemplarily, arrange a movable laser scanning device in the tunnel, set the laser scanning path, such as a serpentine scanning path, and this scanning path should cover the entire range of the geological high-risk area.

[0083] S22. Use the laser scanning device to scan the geological high-risk area, receive the reflected light signal, and record the absorption spectrum data.

[0084] Exemplarily, the laser scanning device emits a laser with a specific wavelength and scans the geological high-risk area according to the set scanning path. For example, methane uses a laser with a wavelength of 1653 nm, and hydrogen sulfide uses a laser with a wavelength of 1578 nm; while scanning, receive the reflected light signal and record the absorption spectrum data.

[0085] S23. Analyze the absorption spectrum data and calculate the harmful gas concentration to obtain a number of discrete point concentration data of harmful gases.

[0086] Exemplarily, for spectral analysis and concentration calculation, based on the TDLAS technology, calculate the gas concentration through the Beer-Lambert law:

[0087] Among them, C is the harmful gas concentration, α is the absorption coefficient, L is the optical path, is the ratio of incident light intensity to transmitted light intensity, and the double-wavelength measurement method (absorption wavelength + reference wavelength) is used to eliminate the temperature and humidity interference. Through the above formula, a number of discrete point concentration data of harmful gases can be obtained.

[0088] S24. Generate a three-dimensional gas concentration model within the geological high-risk area according to a number of discrete point concentration data of harmful gases.

[0089] Exemplarily, the formula for Kriging interpolation is: The meaning of this formula is to predict the gas concentration Z(s0) of the uncollected point s0 based on the spatial correlation of the discrete sampling points Z(s i ).

[0090] The semivariogram defines the relationship between the concentration difference of two points s i and s j and the distance h: N(h) is the number of point pairs at a distance h.

[0091] By solving the Kriging equations, the weight coefficients λ are determined i :

[0092]

[0093] where μ is the Lagrange multiplier and γ(s i , s j ) is the semivariogram between points s i and s j .

[0094] In practical applications, the discrete concentration points Z(s i ) obtained by laser spectroscopy are input into the Kriging algorithm to generate a continuous three-dimensional gas concentration model in the geologically high-risk area.

[0095] S25. Locate the harmful gas leakage source according to the three-dimensional gas concentration model. Correspondingly, the three-dimensional gas concentration model can reflect the distribution of harmful gases in the geologically high-risk area, and the place with the highest harmful gas concentration is the harmful gas leakage source.

[0096] S3. Monitor the harmful gas concentration at the harmful gas leakage source.

[0097] Exemplarily, the laser spectroscopy method is used to monitor the harmful gas concentration at the harmful gas leakage source in real time, which can improve the monitoring accuracy compared with the electrochemical sensor monitoring.

[0098] The method for monitoring the harmful gas concentration at the harmful gas leakage source includes the following steps:

[0099] Arrange laser gas sensors around the harmful gas leakage source, and use the laser gas sensors to collect the harmful gas concentration data at the harmful gas leakage source in real time.

[0100] Exemplarily, install laser gas sensors within a range of 2 - 5 m around the harmful gas leakage source, set the initial sampling frequency to 1 Hz, and the detection accuracy to ±1 ppm; use the laser gas sensors to collect the harmful gas concentration data at the harmful gas leakage source in real time.

[0101] During the monitoring process, the monitoring mode of the laser gas sensor is adjusted according to the fluctuation degree of the harmful gas concentration data collected by the laser gas sensor. For example, when the fluctuation degree of the harmful gas concentration data is within the preset range, it indicates that the leakage rate of the harmful gas is relatively stable. At this time, the sampling frequency of the laser gas sensor can be reduced to the energy-saving monitoring mode of 0.5 Hz to achieve the energy-saving effect; when the fluctuation degree of the harmful gas concentration data exceeds the preset range, it indicates that the leakage rate of the harmful gas fluctuates greatly. The sampling frequency of the laser gas sensor can be increased to the high-frequency monitoring mode of 10 Hz for high-frequency monitoring to improve the accuracy and efficiency of monitoring.

[0102] During the monitoring process, the detection data of geological high-risk areas such as seismic wave data, the determination data of harmful gas leakage sources such as three-dimensional gas concentration models, and the real-time monitoring data of laser gas sensors can also be input into the AI model. The AI model is used to optimize the subsequent layout method of the laser gas sensor to form a closed-loop feedback. Among them, the AI model includes but is not limited to the random forest algorithm.

[0103] S4. Determine whether to generate an alarm message according to the harmful gas concentration, and give an alarm according to the alarm message. The method for determining whether to generate an alarm message according to the harmful gas concentration may include: when the harmful gas concentration is equal to or greater than the first threshold, generating a first alarm message; when the harmful gas concentration is equal to or greater than the second threshold, generating a second alarm message; where the first threshold is less than the second threshold.

[0104] Exemplarily, after the laser gas sensor collects the harmful gas concentration data, it is uploaded to the central control system. A concentration threshold is set in the central control system. If the collected harmful gas concentration exceeds the first threshold, a first alarm message is generated, and an audible and visual alarm is given according to the first alarm message, and the exhaust fan is turned on for ventilation, so as to reduce the harmful gas concentration in the tunnel; if the collected harmful gas concentration exceeds the second threshold, a second alarm message is generated, and the operation area is closed according to the second alarm message, and the emergency response is started.

[0105] S5. Repeat the above operations periodically. Correspondingly, repeating steps S1 to S4 periodically can dynamically adjust the harmful gas leakage source and perform dynamic monitoring of the harmful gas, so as to realize the tracking of the whole life cycle of the harmful gas.

[0106] Exemplarily, steps S1 to S4 are re-executed every 24 hours; when step S1 is re-executed, the geological high-risk area can be re-determined; when step S2 is re-executed, the harmful gas leakage source in the geological high-risk area can be re-determined; when steps S3 and S4 are re-executed, the re-determined harmful gas leakage source can be monitored to collect the harmful gas concentration data in real time.

[0107] The tunnel harmful gas detection method provided by the embodiments of the present application can initially detect geological high-risk areas in the tunnel, determine harmful gas leakage sources within the geological high-risk areas, and continuously monitor the harmful gas concentration at the harmful gas leakage sources, enabling real-time capture of the harmful gas leakage situation and avoiding the problem of sudden gas leakage that cannot be detected in a timely manner. Once the harmful gas concentration reaches the set threshold, an alarm message can be quickly generated and an alarm can be issued, providing timely safety warnings for the personnel and equipment in the tunnel and effectively reducing the safety risks brought by harmful gas leakage.

[0108] Compared with the prior art, by determining the harmful gas leakage sources in the initially detected geological high-risk areas, the present application can establish a direct connection between geological anomalies and harmful gas distribution, thereby more accurately identifying the sources and migration paths of harmful gases. This not only allows the monitoring resources to be concentrated in the areas most likely to have harmful gas leakage, improving the efficiency and pertinence of monitoring, avoiding missed detections or misjudgments caused by insufficient monitoring pertinence, but also effectively reducing the number of monitoring devices deployed and the detection range, shortening the operation cycle, improving the engineering efficiency, and avoiding waste of resources. By training the AI model with historical data, it is also possible to improve the prediction accuracy of the correlation between geological anomalies and gas concentration, and dynamically optimize the deployment density, quantity, and alarm threshold of monitoring devices.

[0109] The embodiments of the present application can divide the functions of the device and the server according to the above method examples. For example, each function can correspond to a respective functional module, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present application is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0110] In the case of dividing each functional module corresponding to each function, Figure 2 shows a possible schematic diagram of the composition of the system involved in the above embodiments. Refer to Figure 2 As shown in the figure, the system 100 may include a detection module 101, a determination module 102, a monitoring module 103, and an alarm module 104.

[0111] Among them, the detection module 101 is used to initially detect geological high-risk areas in the tunnel; the determination module 102 is used to determine harmful gas leakage sources within the geological high-risk areas; the monitoring module 103 is used to monitor the harmful gas concentration at the harmful gas leakage sources; the alarm module 104 is used to determine whether to generate an alarm message based on the harmful gas concentration and issue an alarm according to the alarm message.

[0112] Figure 2The modules in it can also be referred to as units. For example, the detection module can also be called the detection unit. Figure 2 When each module in it is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0113] See Figure 3 , the embodiments of the present application also provide a hardware structure of a system. The system 200 includes a memory 201 and a processor 202; optionally, it further includes a communication interface 203 connected to the processor 202. Among them, the memory 201, the processor 202, and the communication interface 203 are connected through a bus 204.

[0114] The memory 201 can be a read-only memory or other types of static storage devices that can store static information and instructions, a random access memory, or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory, a compact disc read-only memory, or other optical disc storage, optical disc storage, magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer. The embodiments of the present application do not make any restrictions on this.

[0115] The processor 202 can be a central processing unit, a general-purpose processor, a network processor, a digital signal processor, a microprocessor, a microcontroller, a programmable logic device, or any combination thereof. The processor 202 can also be any other device with processing functions, such as a circuit, a device, or a software module. The processor 202 can also include multiple CPUs, and the processor 202 can be a single-core processor or a multi-core processor. Here, the processor 202 can refer to one or more devices, circuits, or processing cores for processing data.

[0116] The memory 201 can exist independently or be integrated with the processor 202. Among them, the memory 201 stores computer program code, and the processor 202 is used for the computer program code stored in the memory 201, so as to implement the tunnel harmful gas detection method provided by the embodiments of the present application.

[0117] The communication interface 203 can be used to communicate with other devices or communication networks. The communication network can be an Ethernet, a wireless access network, a wireless local area network, etc. The communication interface 203 can be a module, a circuit, a transceiver, or any device capable of implementing communication.

[0118] The bus 204 can be a peripheral component interconnect standard bus or an extended industry standard architecture bus, etc. The bus 204 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 3It is represented by only one line, but it does not mean that there is only one bus or one type of bus.

[0119] An embodiment of the present application further provides a computer storage medium, including computer instructions. When the computer instructions run on a computer, the computer is made to execute the tunnel harmful gas detection method provided in the above embodiment. Among them, the storage medium can be any available medium that the computer can access or a data storage device such as a server or a data center that includes one or more media that can be integrated. For example, the available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0120] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application.

Claims

1. A method for detecting harmful gases in a tunnel, characterized in that, Including: Preliminarily detecting geological high-risk areas in the tunnel; Determining harmful gas leakage sources within the geological high-risk areas; Monitoring the concentration of harmful gases at the harmful gas leakage sources; Determining whether to generate an alarm message based on the concentration of harmful gases and issuing an alarm according to the alarm message; Periodically repeating the above operations.

2. The tunnel harmful gas detection method according to claim 1, characterized in that, The preliminary detection of geological high-risk areas in the tunnel includes: Arranging a seismic source device and seismic wave receivers in the tunnel; Triggering the seismic source device to emit seismic waves and using the seismic wave receivers to collect reflected wave signals; Processing the reflected wave signals and generating a geological anomaly distribution map; Generating geological high-risk areas based on the geological anomaly distribution map.

3. The tunnel harmful gas detection method according to claim 2, characterized in that, The processing of the reflected wave signals and generating a geological anomaly distribution map includes: Performing wavelet transform on the reflected wave signals to separate high-frequency noise; Conducting envelope analysis on the waveform to detect waveform distortion characteristics; Determining geological anomalies based on the waveform distortion characteristics and generating a geological anomaly distribution map.

4. The tunnel harmful gas detection method according to claim 1, 2 or 3, characterized in that The determination of harmful gas leakage sources within the geological high-risk areas includes: Arranging a laser scanning device in the tunnel and setting a scanning path; Using the laser scanning device to scan the geological high-risk areas, receiving reflected light signals, and recording absorption spectrum data; Analyzing the absorption spectrum data and calculating the concentration of harmful gases to obtain concentration data of several discrete points of harmful gases; Generating a three-dimensional gas concentration model within the geological high-risk areas based on the concentration data of several discrete points of harmful gases; Locating the harmful gas leakage sources based on the three-dimensional gas concentration model.

5. The tunnel harmful gas detection method according to claim 1, 2 or 3, characterized in that, The monitoring of the concentration of harmful gases at the harmful gas leakage sources includes: Arranging laser gas sensors around the harmful gas leakage sources and using the laser gas sensors to collect real-time concentration data of harmful gases at the harmful gas leakage sources; Adjusting the monitoring mode of the laser gas sensors according to the fluctuation degree of the concentration data of harmful gases collected by the laser gas sensors.

6. The tunnel harmful gas detection method according to claim 5, characterized in that, The monitoring of the concentration of harmful gases at the harmful gas leakage sources further includes: Inputting the detection data of the geological high-risk areas, the determination data of the harmful gas leakage sources, and the real-time monitoring data of the laser gas sensors into an AI model, and using the AI model to optimize the subsequent arrangement method of the laser gas sensors.

7. The tunnel harmful gas detection method according to claim 1, 2 or 3, characterized in that, The determination of whether to generate an alarm message based on the concentration of harmful gases includes: When the concentration of harmful gases is equal to or greater than the first threshold, generating a first alarm message; When the concentration of harmful gases is equal to or greater than the second threshold, generating a second alarm message; Wherein, the first threshold is less than the second threshold.

8. A harmful gas detection system for tunnels, characterized in that, Including: A detection module for preliminarily detecting geological high-risk areas in the tunnel; A determination module for determining harmful gas leakage sources within the geological high-risk areas; A monitoring module for monitoring the concentration of harmful gases at the harmful gas leakage sources; An alarm module for determining whether to generate an alarm message based on the concentration of harmful gases and issuing an alarm according to the alarm message.

9. A harmful gas detection system for tunnels, characterized in that, Including a memory and a processor; The memory stores instructions executable by the processor; When the processor is configured to execute the instructions, the system implements the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Including computer instructions, when the computer instructions run on a computer, the computer executes the method according to any one of claims 1 to 7.

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